{
    "cells": [
        {
            "cell_type": "markdown",
            "source": [
                "# 基础数据操作"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 354,
            "source": [
                "import torch as tc"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 355,
            "source": [
                "x=tc.empty(5,3)\n",
                "print(x)"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "tensor([[-2.1950e+305,  9.5355e-322,  4.6719e-310],\n",
                        "        [ 4.6719e-310,  7.9051e-323,  3.9525e-323],\n",
                        "        [ 2.3715e-322,  2.3715e-322,  4.6727e-310],\n",
                        "        [  4.1185e+02,   3.7133e+02,   4.1535e+02],\n",
                        "        [-2.1950e+305,  4.7924e-322,  4.6719e-310]])\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 356,
            "source": [
                "x2=tc.rand(5,3)\n",
                "print(x2)"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "tensor([[0.7083, 0.9053, 0.6125],\n",
                        "        [0.0286, 0.1158, 0.7717],\n",
                        "        [0.2689, 0.6795, 0.3106],\n",
                        "        [0.5831, 0.2201, 0.4018],\n",
                        "        [0.0271, 0.0339, 0.0443]])\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 357,
            "source": [
                "x3=tc.tensor([5.5,3])\n",
                "print(x3)"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "tensor([5.5000, 3.0000])\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 358,
            "source": [
                "x4=x.new_ones(5,3,dtype=tc.float64)\n",
                "print(x4)"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "tensor([[1., 1., 1.],\n",
                        "        [1., 1., 1.],\n",
                        "        [1., 1., 1.],\n",
                        "        [1., 1., 1.],\n",
                        "        [1., 1., 1.]])\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "## autograd"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 359,
            "source": [
                "x=tc.ones(2,2,requires_grad=True)"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 360,
            "source": [
                "print(x.grad_fn)"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "None\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 361,
            "source": [
                "y=x+2\n",
                "print(y)\n",
                "print(y.grad_fn)"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "tensor([[3., 3.],\n",
                        "        [3., 3.]], grad_fn=<AddBackward0>)\n",
                        "<AddBackward0 object at 0x7f62404c89b0>\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 362,
            "source": [
                "z=y*y*3\n",
                "out=z.mean()\n",
                "print(z,out)"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "tensor([[27., 27.],\n",
                        "        [27., 27.]], grad_fn=<MulBackward0>) tensor(27., grad_fn=<MeanBackward0>)\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 363,
            "source": [
                "out.backward()"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 364,
            "source": [
                "x.grad"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "tensor([[4.5000, 4.5000],\n",
                            "        [4.5000, 4.5000]])"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 364
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "## 累加机制 \n",
                "反向传播中grad是累加的，因此新的反向传播时需要进行清0"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 365,
            "source": [
                "out2=x.sum()\n",
                "out2.backward()\n",
                "print(x.grad)"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "tensor([[5.5000, 5.5000],\n",
                        "        [5.5000, 5.5000]])\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "可以看到 sum(x) 的梯度实际上应该是x_i，但是由于累加机制变成了4.5+1"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 366,
            "source": [
                "out3=x.sum()\n",
                "x.grad.data.zero_()\n",
                "out3.backward()\n",
                "print(x.grad)"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "tensor([[1., 1.],\n",
                        "        [1., 1.]])\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "此时结果正确"
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "## backward 的输入参数问题\n",
                "\n",
                "当backward对象为标量时，不需要输入参数，或者理解为输入为1,当对象为张量时，需要输入一个相同尺寸的张量，避免张量对张量求导。\n",
                "\n",
                "输入张量与对象相乘转化为标量，在进行backward操作。\n",
                "\n"
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "### 实例"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 367,
            "source": [
                "x=tc.tensor([1.,2.,3.,4.],requires_grad=True)\n",
                "y=2*x\n",
                "z=y.view(2,2)\n",
                "print(z)"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "tensor([[2., 4.],\n",
                        "        [6., 8.]], grad_fn=<ViewBackward>)\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 368,
            "source": [
                "v=tc.tensor([[1.,0.1],[0.01,0.001]],dtype=tc.float)\n",
                "z.backward(v)\n",
                "print(x.grad)"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "tensor([2.0000, 0.2000, 0.0200, 0.0020])\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "## 中断梯度追踪\n",
                "\n",
                "with torch.no_grad(): \n",
                "\n",
                "在这个代码块中包含的运算不会被梯度追踪。"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 369,
            "source": [
                "x=tc.tensor(1.,requires_grad=True)\n",
                "y1=x**2\n",
                "with tc.no_grad():\n",
                "    y2=x**3\n",
                "y=y1+y2\n",
                "y.backward()\n",
                "print(x.grad)"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "tensor(2.)\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "## autogard之外的数据修改\n",
                "\n",
                "通过修改tensor.data 在不被autogard追踪的情况下修改tensor数据。"
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "# 线性回归"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 370,
            "source": [
                "from IPython import display\n",
                "from matplotlib import pyplot as plt\n",
                "import numpy as  np \n",
                "import random"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 371,
            "source": [
                "# W=[2,-3.4] b=4.3\n",
                "# y=Xw+b+e e belongs to a normalized distribution with 0 mean 0.01 standard error\n",
                "\n",
                "num_inputs=2\n",
                "num_examples=1000\n",
                "\n",
                "true_w=[2,-3.4]\n",
                "true_b=4.2\n",
                "\n",
                "features=tc.from_numpy(np.random.normal(0,1,(num_examples,num_inputs))) # 生成干扰项\n",
                "\n",
                "labels= true_w[0]*features[:,0]+true_w[1]*features[:,1]+true_b\n",
                "labels+=tc.from_numpy(np.random.normal(0,0.01,size=labels.size()))\n",
                "\n",
                "print(features[0],labels[0])"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "tensor([ 1.4310, -3.0725]) tensor(17.5183)\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 372,
            "source": [
                "# 矢量图显示\n",
                "\n",
                "def use_svg_display():\n",
                "    display.set_matplotlib_formats('svg')"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 373,
            "source": [
                "# 设置图片尺寸\n",
                "def setfigsize(figsize=(3.5,2.5)):\n",
                "    use_svg_display()\n",
                "    plt.rcParams['figure.figsize']=figsize"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 374,
            "source": [
                "setfigsize()\n",
                "\n",
                "plt.scatter(features[:,1].numpy(),labels.numpy(),1)"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "<matplotlib.collections.PathCollection at 0x7f6240695470>"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 374
                },
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": [
                            "<Figure size 252x180 with 1 Axes>"
                        ],
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                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "## 读取数据"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 375,
            "source": [
                "def data_iter(batch_size,features,labels):\n",
                "    num_examples=len(features)\n",
                "    indice=list(range(num_examples))   \n",
                "    random.shuffle(indice)  #  shuffle随即打乱输入list的顺序\n",
                "    for i in range(0,num_examples,batch_size):\n",
                "        j=tc.LongTensor(indice[i:min(i+batch_size,num_examples)])\n",
                "        yield features.index_select(0,j),labels.index_select(0,j)  # yield  返回函数数值并且继续执行程序\n"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "这里利用了python 的yield特性创造了迭代器"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 376,
            "source": [
                "batch_size=10\n",
                "for X,y in data_iter(batch_size,features,labels):\n",
                "    print(X,y)\n",
                "    break"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "tensor([[-0.2620,  1.4240],\n",
                        "        [ 0.1880,  0.7456],\n",
                        "        [ 1.3275,  0.3121],\n",
                        "        [ 1.0209,  1.3102],\n",
                        "        [ 0.1913,  0.0309],\n",
                        "        [-0.9256, -0.9987],\n",
                        "        [-0.4534,  0.7280],\n",
                        "        [-0.5747,  0.6242],\n",
                        "        [-0.4134, -1.5292],\n",
                        "        [ 0.0959,  0.8168]]) tensor([-1.1573,  2.0270,  5.7804,  1.7839,  4.4804,  5.7516,  0.8148,  0.9330,\n",
                        "         8.5815,  1.5994])\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "测试小批量样本采集的效果"
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "## 初始化权重"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 377,
            "source": [
                "w=tc.tensor(np.random.normal(0,0.01,(num_inputs,1)),dtype=tc.float64,requires_grad=True)\n",
                "b=tc.zeros(1,dtype=float,requires_grad=True)\n"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "## 回归表达"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 378,
            "source": [
                "def linreg(X,w,b):\n",
                "    return tc.mm(X,w)+b"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "## 平方损失"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 379,
            "source": [
                "def squared_loss(y_hat,y):\n",
                "    return (y_hat-y.view(y_hat.size()))**2"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "## 小批量随即下降算法 \n",
                "lr 为 learning rate\n",
                "与公式吻合"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 380,
            "source": [
                "def sgd(params,lr,batch_size):\n",
                "    for param in params:\n",
                "        param.data-=lr*param.grad/batch_size  "
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "## 训练模型"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 381,
            "source": [
                "lr=0.03\n",
                "num_epochs=3\n",
                "net=linreg\n",
                "loss=squared_loss\n",
                "\n",
                "\n",
                "for epoch in range(num_epochs):\n",
                "    for X,y in data_iter(batch_size,features,labels):\n",
                "        # print(X.dtype,y.dtype)\n",
                "        l=loss(net(X,w,b),y).sum()\n",
                "        l.backward()\n",
                "        sgd([w,b],lr,batch_size)\n",
                "        w.grad.data.zero_()\n",
                "        b.grad.data.zero_()\n",
                "    train_l=loss(net(features,w,b),labels)\n",
                "    print('epoch %d,loss %f' % (epoch+1,train_l.mean().item()))"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "epoch 1,loss 0.000222\n",
                        "epoch 2,loss 0.000105\n",
                        "epoch 3,loss 0.000104\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "查看训练参数与实际参数"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 382,
            "source": [
                "print(true_b,':',b)\n",
                "print(true_w,':',w)"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "4.2 : tensor([4.1997], requires_grad=True)\n",
                        "[2, -3.4] : tensor([[ 2.0001],\n",
                        "        [-3.3998]], requires_grad=True)\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "# softmax回归"
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "## 依赖"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 383,
            "source": [
                "import torch\n",
                "import torchvision\n",
                "from torchvision import transforms\n",
                "import matplotlib.pyplot as plt\n",
                "import time\n",
                "import sys \n",
                "import numpy as np\n",
                "sys.path.append('..')\n",
                "from IPython import display"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "## 数据读取\n",
                "由于 mnist数据为data.Dataset的实例，因此可以使用dataloader读取"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 384,
            "source": [
                "mnist_train=torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST',train=True,download=True,transform=transforms.ToTensor())\n",
                "mnist_test=torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST',train=False,download=True,transform=transforms.ToTensor())\n"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "## 定义演示相关函数"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 387,
            "source": [
                "\n",
                "def get_fashion_mnist_labels(labels):\n",
                "    text_labels=['t-shirts','trouser','pullover','dress','coat','sandal','shirt','sneaker','bag','ankle','boot']\n",
                "    return [text_labels[int(i)] for i in labels]\n",
                "def show_fashion_mnist(images,labels):\n",
                "    use_svg_display()\n",
                "    _,figs=plt.subplots(1,len(images),figsize=(12,12))\n",
                "    for f,img,lbl in zip(figs,images,labels):\n",
                "        f.imshow(img.view((28,28)).numpy())\n",
                "        f.set_title(lbl)\n",
                "        f.axes.get_xaxis().set_visible(False)\n",
                "        f.axes.get_yaxis().set_visible(False)\n",
                "    plt.show()"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "测试"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 388,
            "source": [
                "X,y=[],[]\n",
                "for i in range(15):\n",
                "    X.append(mnist_train[i][0])\n",
                "    y.append(mnist_train[i][1])\n",
                "show_fashion_mnist(X,get_fashion_mnist_labels(y))\n"
            ],
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": [
                            "<Figure size 864x864 with 15 Axes>"
                        ],
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                        "needs_background": "light"
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                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "## 定义迭代器"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "source": [
                "device=torch.device('cuda' if torch.cuda.is_available() else 'cpu') # cuda加速\n",
                "train_iter=torch.utils.data.DataLoader(mnist_train,batch_size=batch_size,shuffle=True,num_workers=8)\n",
                "test_iter=torch.utils.data.DataLoader(mnist_test,batch_size=batch_size,shuffle=True,num_workers=8) # 多线程\n",
                "# start=time.time()\n",
                "# for X,y in train_iter:\n",
                "#     continue\n",
                "# print('%.2f sec' % (time.time()-start))\n",
                "#print(device)"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "## 定义模型\n",
                "\n",
                "图片大小为28*28 即 有28*28个像素点，每一个都为一个x输入，并为之设置权重w,W*X+b作为模型输出。对输出进行softmax运算（转化到0-1区间并且不改变单调性）"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "source": [
                "\n",
                "numinputs=784  # 28*28\n",
                "numoutputs=10  # 10个种类\n",
                "torch.set_default_dtype(torch.float64)\n",
                "W=torch.tensor(np.random.normal(0,0.01,(numinputs,numoutputs)))\n",
                "b=torch.zeros(numoutputs)\n",
                "#W=torch.tensor(np.random.normal(0,0.01,(numinputs,numoutputs))).to(device)\n",
                "#b=torch.zeros(numoutputs).to(device)\n",
                "\n",
                "# torch.cuda.is_available()\n",
                "# torch.__version__\n",
                "# print(torch.version.cuda)\n",
                "\n",
                "W.requires_grad_(requires_grad=True)  # 梯度追踪\n",
                "b.requires_grad_(requires_grad=True)\n"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "## 定义softmax运算符"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "source": [
                "def softmax(X):\n",
                "    X_exp=X.exp()\n",
                "    parttion=X_exp.sum(dim=1,keepdim=True)\n",
                "    # print(parttion.shape)\n",
                "    # print(X_exp.shape)\n",
                "    return X_exp/parttion\n",
                "# X=torch.rand((2,5))\n",
                "def net(X):\n",
                "    return softmax(torch.mm(X.view((-1,numinputs)),W)+b)\n",
                "# test\n",
                "# for X,y in train_iter:\n",
                "\n",
                "#     net(X)\n",
                "#     break"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "## 交叉熵\n",
                "\n"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "source": [
                "def cross_entropy(y_hat,y):\n",
                "    return - torch.log(y_hat.gather(1,y.view(-1,1)))\n"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "## 准确度"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "source": [
                "def accuracy(y_hat,y):\n",
                "    return (y_hat.argmax(dim=1)==y).float().mean().item() # argmax 返回dim轴上最大元素的位置 ，item 将tensor转化为数值。"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "source": [
                "\n",
                "\n",
                "def evaluate_accuracy(data_iter,net):\n",
                "    acc_sum,n=0.0,0\n",
                "    for X,y in data_iter:\n",
                "        acc_sum+= (net(X).argmax(dim=1)==y).float().sum().item()\n",
                "        n+=y.shape[0]\n",
                "    return acc_sum/n\n"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "source": [
                "num_epochs,lr=5,0.1\n",
                "batch_size=8192\n",
                "def train_ch3(net,train_iter,test_iter,loss, num_epochs,batch_size,params=None,lr=None,optimizer=None):\n",
                "    for epoch in range(num_epochs):\n",
                "        train_l_sum,train_acc_sum,n=0.0,0.0,0\n",
                "        for X,y in train_iter:\n",
                "\n",
                "            y_hat=net(X)\n",
                "            # break\n",
                "            l=loss(y_hat,y).sum()\n",
                "            if optimizer is not None:\n",
                "                optimizer.zero_grad()\n",
                "            elif params is not None and params[0].grad is not None:\n",
                "                for param in params :\n",
                "                    param.grad.data.zero_()\n",
                "            l.backward()\n",
                "            if optimizer is None:\n",
                "                sgd(params,lr,batch_size)\n",
                "            else :\n",
                "                optimizer.step()\n",
                "            train_l_sum+=l.item()\n",
                "            train_acc_sum+= (y_hat.argmax(dim=1)==y).sum().item()\n",
                "            n+=y.shape[0]\n",
                "        test_acc=evaluate_accuracy(test_iter,net)\n",
                "        print('epoch %d ,loss %.4f , train acc %.3f ,test acc %.3f '% (epoch +1,train_l_sum/n,train_acc_sum/n,test_acc))\n",
                "train_ch3(net,train_iter,test_iter,cross_entropy,num_epochs,batch_size,[W,b],lr)"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "source": [
                "X,y=iter(test_iter).next()\n",
                "true_labels=get_fashion_mnist_labels(y.numpy())\n",
                "pred_labels=get_fashion_mnist_labels(net(X).argmax(dim=1).numpy())\n",
                "titles=[true+'\\n'+pred for true,pred in zip(true_labels,pred_labels)]\n",
                "show_fashion_mnist(X[0:9],titles[0:9])"
            ],
            "outputs": [],
            "metadata": {}
        }
    ],
    "metadata": {
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